Blind synchronization in wireless sensor networks with application to speech enhancement

Cherkassky, Dani; Gannot, Sharon

doi:10.1109/iwaenc.2014.6954003

Cited by 17 publications

(6 citation statements)

References 13 publications

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“…Correlation-based methods are known to be sensitive to source motion [5,6]. Movement of the microphones themselves is fatal, since sample rate offsets and constant-velocity motion induce nearly identical linear phase shifts [13]. Figure 2 shows the performance of a blind sample rate estimation algorithm [6] in a cocktail party scenario.…”

Section: Asynchronous Array Processingmentioning

confidence: 99%

Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling

Corey

Singer

2018

2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC)

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We consider the problem of separating speech sources captured by multiple spatially separated devices, each of which has multiple microphones and samples its signals at a slightly different rate. Most asynchronous array processing methods rely on sample rate offset estimation and resampling, but these offsets can be difficult to estimate if the sources or microphones are moving. We propose a source separation method that does not require offset estimation or signal resampling. Instead, we divide the distributed array into several synchronous subarrays. All arrays are used jointly to estimate the timevarying signal statistics, and those statistics are used to design separate time-varying spatial filters in each array. We demonstrate the method for speech mixtures recorded on both stationary and moving microphone arrays.

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Section: Asynchronous Array Processingmentioning

confidence: 99%

Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling

Corey

Singer

2018

2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC)

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“…A similar approach to the mentioned above was proposed in [6], in this paper only the sampling rate difference is investigated, but not delay differences.…”

Section: Review Of Current Approaches In A/d Converter Synchronizmentioning

confidence: 99%

Investigation of electric network frequency for synchronization of low cost and wireless sound cards

Golokolenko

Schuller

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-An electric network frequency (ENF) signal can be found in multimedia recordings due to propagation from the power grid. There a variety of applications based on ENF signal use, such as video and audio stream synchronization, and origin determination of multimedia recordings. In this paper, we propose the use of ENF to synchronize real-time audio streams from different, non-synchronized sound devices, for instance from wireless sound cards or low cost USB sound cards. Synchronization of separate audio streams can be achieved by aligning their embedded ENF signals. Our goal is to find out how accurate a synchronization using ENF at different SNR levels can be. We show simulation and real-time experimental results of audio stream synchronization. We found that with sufficient ENF level we can achieve an accuracy of the estimated delay difference of four samples at 44.1 kHz sampling rate.

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“…Generally, two groups of synchronization architectures can be distinguished, the first of which is described as open-loop (OL) synchronization. Here, SRO and STO are blindly estimated from just the available acoustic signals by different algorithms like coherence drift [13][14][15], maximum-likelihood [16,17], correlation-based [18][19][20], or double-cross-correlation processing (DXCP) [21]. Afterwards, raw signals are resampled by ASRC, e.g., [22][23][24].…”

Section: Introduction and Relation To Prior Workmentioning

confidence: 99%

A Double-cross-correlation Processor for Blind Sampling Rate Offset Estimation in Acoustic Sensor Networks

Chinaev

Thune

Enzner

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Distributed hardware of acoustic sensor networks bears inconsistency of local sampling frequencies, which is detrimental to signal processing. Fundamentally, sampling rate offset (SRO) nonlinearly relates the discrete-time signals acquired by different sensor nodes. As such, retrieval of SRO from the available signals requires nonlinear estimation, like double-cross-correlation processing (DXCP), and frequently results in biased estimation. SRO compensation by asynchronous sampling rate conversion (ASRC) on the signals then leaves an unacceptable residual. As a remedy to this problem, multistage procedures have been devised to diminish the SRO residual with multiple iterations of SRO estimation and ASRC over the entire signal. This paper converts the mechanism of offline multi-stage processing into a continuous feedback-control loop comprising a controlled ASRC unit followed by an online implementation of DXCPbased SRO estimation. To support the design of an optimum internal model control unit for this closed-loop system, the paper deploys an analytical dynamical model of the proposed online DXCP. The resulting control architecture then merely applies a single treatment of each signal frame, while efficiently diminishing SRO bias with time. Evaluations with both speech and Gaussian input demonstrate that the high accuracy of multi-stage processing is maintained at the low complexity of single-stage (open-loop) processing.

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Blind synchronization in wireless sensor networks with application to speech enhancement

Cited by 17 publications

References 13 publications

Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling

Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling

Investigation of electric network frequency for synchronization of low cost and wireless sound cards

A Double-cross-correlation Processor for Blind Sampling Rate Offset Estimation in Acoustic Sensor Networks

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